The present disclosure relates to techniques for computing relevancy between tax information.
A number of companies presently provide software to facilitate accounting and income-tax preparation services. For example, Intuit, Inc. (of Mountain View, Calif.) presently provides income-tax preparation software to millions of individuals and businesses. This software is centered on a so-called “tax engine” that encodes tax knowledge, such as tax-related logic and associated calculations. In order to both accumulate and refresh this tax knowledge, a large group of income-tax experts reviews hundreds of official income-tax documents (including tax forms, instructions, publications, etc.) every year. These income-tax documents include thousands of pages and millions of words. Consequently, reviewing these income-tax documents is an extremely cumbersome, time-consuming and expensive manual process, which significantly increases software-development expense.
Furthermore, the manual review of the income-tax documents is prone to error and typically results in a quasistatic tax engine, i.e., the tax engine is only updated infrequently, such as once a year. Because the tax engine is quasistatic, it is often difficult to provide value-added services, especially in dynamic environments (such as online), or to adapt the income-tax software to changing customer needs. These limitations can degrade the customer experience, and thus can adversely impact customer retention and profitability.